Rachman, Fauzi (2025) RANCANG BANGUN APLIKASI KLASIFIKASI JENIS TANAMAN BUGENVIL MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) (Studi Kasus: Kios Bunga Rabiku Florist). S1 / D3 thesis, Universitas Kuningan.

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Official URL: https://rama.uniku.ac.id

Abstract

Bugenvil merupakan salah satu tanaman hias yang cukup digemari dan memiliki beragam jenis dengan ciri morfologis yang sering kali tampak serupa. Kemiripan ini kerap menyulitkan proses pengenalan jenis tanaman secara konvensional, terutama bagi penjual maupun pembeli di Rabiku Florist. Penelitian ini bertujuan untuk mengembangkan sebuah aplikasi Android yang mampu mengelompokkan jenis bugenvil secara otomatis dengan memanfaatkan algoritma Convolutional Neural Network (CNN). Pengembangan sistem menggunakan metode Rapid Application Development (RAD), dengan model arsitektur MobileNetV2 dan integrasi melalui framework TensorFlow Lite agar dapat dijalankan pada perangkat mobile. Aplikasi ini dirancang untuk mengenali lima jenis tanaman bugenvil melalui citra digital yang diperoleh dari kamera atau galeri pengguna. Berdasarkan hasil implementasi, sistem terbukti mampu melakukan klasifikasi jenis tanaman dengan kinerja yang optimal dan memberikan informasi yang cukup akurat kepada pengguna. Aplikasi ini diharapkan dapat menjadi alat bantu yang mudah digunakan oleh masyarakat dan pelaku usaha dalam mengenali jenis bugenvil.

Bougainvillea is one of the most popular ornamental plants, featuring a variety of types with morphological characteristics that often appear very similar. This resemblance frequently complicates the conventional identification process, particularly for sellers and buyers at Rabiku Florist. This study aims to develop an Android application capable of automatically classifying different bougainvillea types using a Convolutional Neural Network (CNN) algorithm. The system is developed using the Rapid Application Development (RAD) methodology, leveraging the MobileNetV2 architecture and integrating it with the TensorFlow Lite framework to ensure compatibility with mobile devices. The application is designed to identify five types of bougainvillea using digital images captured via the device’s camera or selected from the user’s gallery. Based on implementation results, the system demonstrates strong classification performance and delivers accurate information to users. This application is intended to serve as a practical and user-friendly tool for both the general public and businesses in accurately identifying bougainvillea species.

Item Type: Thesis (S1 / D3)
Uncontrolled Keywords: Kata Kunci : Klasifikasi Citra, Bugenvil, Convolutional Neural Network, MobileNetV2, Android. Keywords : Image Classification, Bougainvillea, Convolutional Neural Network, MobileNetV2, Android.
Subjects: T Technology > T Technology (General)
Divisions: Fakultas Ilmu Komputer > S1 Teknik Informatika
Depositing User: S.Kom Fauzi Rachman
Date Deposited: 25 Aug 2025 03:36
Last Modified: 25 Aug 2025 03:36
URI: https://rama.uniku.ac.id/id/eprint/3209

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